A novel nomogram containing efficacy indicators to predict axillary pathologic complete response after neoadjuvant systemic therapy in breast cancer

Shi, Wenjie; Huang, Xiaofeng; Wang, Ye; Wan, Xinyu; He, Jinzhi; Xu, Yinggang; Zhang, Weiwei; Chen, Rui; Xu, Lu; Zha, Xiaoming; Wang, Jue

doi:10.3389/fendo.2022.1042394

Cited by 4 publications

(6 citation statements)

References 31 publications

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“…As shown in our data, the surgical methods for IBC did not differ in the with or without MC groups. ANM is one of the most significant predictor of overall survival in patients with IBC 41‐43 . However, patients with MC were more likely to develop ANM, indicating a relatively poor prognosis.…”

Section: Discussionmentioning

confidence: 99%

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Clinical and pathological features analysis of invasive breast cancer with microcalcification

Tian

Gui

et al. 2023

Cancer Medicine

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Purpose Microcalcification (MC) is a valuable diagnostic indicator to detect invasive breast cancer (IBC). This study aimed to determine the clinicopathological features of IBC with MC and detect biomarkers related to the potential mechanism of the MC formation in IBC. Methods Data from 364 patients with IBC were collected for the clinical characteristic analysis. The analysis of clinical data helped us to establish a predictive model of axillary node metastasis (ANM) before surgery. In addition, 49 tissue samples of IBC patients were collected to test the protein levels of osteocalcin (OCN) and hypoxia‐inducible factor‐1α (HIF‐1α) by immunohistochemistry. Results Significant differences were observed in tumor size, age, ANM, HER2+, TNM stage, and mutant P53 between samples from IBC patients with MC and samples from IBC patients without MC. Younger age, a larger tumor size, a higher number of childbirths, and MC were independent predictors for ANM in IBC. HIF‐1α protein level was higher in tumor tissue than in normal tissue. High protein levels of OCN and HIF‐1α are related to the complication of MC in IBC. Of the patients that exhibited high HIF‐1α protein levels, the percentage of high OCN protein levels was larger in patients with ANM. Conclusion Based on this study, we concluded that patients with MC had a comparatively poor prognosis. MC was an independent predictive factor associated with the risk of ANM. High protein levels of OCN and HIF‐1α were associated with MC and ANM, which were also related to poor prognosis. OCN and HIF‐1α had a positive correlation in IBC.

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Section: Discussionmentioning

confidence: 99%

“…ANM is one of the most significant predictor of overall survival in patients with IBC. 41 , 42 , 43 However, patients with MC were more likely to develop ANM, indicating a relatively poor prognosis. Therefore, the choice of surgical option should be more inclined to mastectomy.…”

Section: Discussionmentioning

confidence: 99%

Clinical and pathological features analysis of invasive breast cancer with microcalcification

Tian

Gui

et al. 2023

Cancer Medicine

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“…Lesion diameters in the CT images could directly reflect tumor burden. There is growing evidence supporting the use of tumor diameters in CT scans to predict the responsivity to neoadjuvant chemotherapy and immune therapy [ 34 , 35 ]. However, the immune therapy effect may precede any observable change of the tumor size [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients

Caii,

Wu,

Guo

et al. 2024

Cancer Immunol Immunother

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Background The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunotherapy response, based on the integration of deep learning and habitat radiomics in patients with advanced non-small cell lung cancer (NSCLC). Methods Independent patient cohorts from three medical centers were enrolled for training (n = 164) and test (n = 82). Habitat imaging radiomics features were derived from sub-regions clustered from individual’s tumor by K-means method. The deep learning features were extracted based on 3D ResNet algorithm. Pearson correlation coefficient, T test and least absolute shrinkage and selection operator regression were used to select features. Support vector machine was applied to implement deep learning and habitat radiomics, respectively. Then, a combination model was developed integrating both sources of data. Results The combination model obtained a strong well-performance, achieving area under receiver operating characteristics curve of 0.865 (95% CI 0.772–0.931). The model significantly discerned high and low-risk patients, and exhibited a significant benefit in the clinical use. Conclusion The integration of deep-leaning and habitat radiomics contributed to predicting response to immunotherapy in patients with NSCLC. The developed integration model may be used as potential tool for individual immunotherapy management.

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“…These results are consistent with reported rates, ranging between 27 and 57%. 1 – 3 , 32 – 36 As the axillary pCR rate varies with receptor subtype, patient selection for NACT and inclusion in studies may explain the variation between studies.…”

Section: Discussionmentioning

confidence: 99%

Residual Axillary Metastases in Node-Positive Breast Cancer Patients After Neoadjuvant Treatment: A Register-Based Study

Munck,

Jensen,

Vejborg

et al. 2024

Ann Surg Oncol

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Background Lymph node (LN) metastasis after neoadjuvant chemotherapy (NACT) generally warrants axillary lymph node dissection, which opposes guidelines of upfront surgery in many cases. We investigated the risk of having additional metastases in the axilla when the LNs removed by targeted axillary dissection (TAD) harbored metastases after NACT. We aimed to identify subgroups suitable for de-escalated axillary treatment. Methods This register-based study used data from the Danish Breast Cancer Cooperative Group database. Data were analyzed with logistic regression models. The primary outcome was the metastatic burden in non-TAD LNs in patients with positive TAD LNs after NACT. Results Among 383 patients, < 66.6% positive TAD LNs (adjusted odds ratio [OR] 0.34, 95% confidence interval [CI] 0.17–0.62), only isolated tumor cells (ITCs) [OR 0.11, 95% CI < 0.01–0.82], and breast pathological complete response (pCR) [OR 0.07, 95% CI < 0.01–0.56] were associated with a low risk of having more than three positive non-TAD LNs. In 315 patients with fewer than three positive non-TAD LNs, the proportion of positive TAD LNs (OR 0.45, 95% CI 0.27–0.76 for 33.3–66.6% vs. > 66.6%), size of the TAD LN metastasis (OR 0.14, 95% CI 0.04–0.54 for ITC vs. macrometastasis), tumor size at diagnosis (OR 0.30, 95% CI 0.15–0.64 for 20–49 mm vs. ≥ 50 mm) and breast pCR (OR 0.38, 95% CI 0.15–0.96) were associated with residual LN metastases in the axilla. Conclusions Breast pCR or ITC only in TAD LNs can, with reasonable certainty, preclude more than three positive non-TAD LNs. Additionally, patients with only ITCs in the TAD LN had a low risk of having any non-TAD LN metastases after NACT. De-escalated axillary treatment may be considered in both subgroups.

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A novel nomogram containing efficacy indicators to predict axillary pathologic complete response after neoadjuvant systemic therapy in breast cancer

Cited by 4 publications

References 31 publications

Clinical and pathological features analysis of invasive breast cancer with microcalcification

Clinical and pathological features analysis of invasive breast cancer with microcalcification

Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients

Residual Axillary Metastases in Node-Positive Breast Cancer Patients After Neoadjuvant Treatment: A Register-Based Study

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